Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations260920
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory137.9 MiB
Average record size in memory554.0 B

Variable types

Numeric7
Categorical4
Text5

Alerts

batsman_runs is highly overall correlated with total_runsHigh correlation
dismissal_kind is highly overall correlated with is_wicketHigh correlation
is_wicket is highly overall correlated with dismissal_kindHigh correlation
total_runs is highly overall correlated with batsman_runsHigh correlation
is_wicket is highly imbalanced (71.5%) Imbalance
dismissal_kind is highly imbalanced (89.3%) Imbalance
over has 13906 (5.3%) zeros Zeros
batsman_runs has 103940 (39.8%) zeros Zeros
extra_runs has 246795 (94.6%) zeros Zeros
total_runs has 90438 (34.7%) zeros Zeros

Reproduction

Analysis started2025-03-13 03:14:48.173070
Analysis finished2025-03-13 03:14:59.833601
Duration11.66 seconds
Software versionydata-profiling vv4.14.0
Download configurationconfig.json

Variables

match_id
Real number (ℝ)

Distinct1095
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean907066.51
Minimum335982
Maximum1426312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-03-12T22:14:59.908135image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum335982
5-th percentile336039
Q1548334
median980967
Q31254066
95-th percentile1422135
Maximum1426312
Range1090330
Interquartile range (IQR)705732

Descriptive statistics

Standard deviation367991.28
Coefficient of variation (CV)0.40569382
Kurtosis-1.5257974
Mean907066.51
Median Absolute Deviation (MAD)331230
Skewness-0.14529399
Sum2.3667179 × 1011
Variance1.3541758 × 1011
MonotonicityIncreasing
2025-03-12T22:15:00.013066image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1216517 269
 
0.1%
392190 267
 
0.1%
1426268 265
 
0.1%
1082625 263
 
0.1%
729315 262
 
0.1%
829737 262
 
0.1%
598004 261
 
0.1%
1254077 260
 
0.1%
1426288 260
 
0.1%
1359480 260
 
0.1%
Other values (1085) 258291
99.0%
ValueCountFrequency (%)
335982 225
0.1%
335983 248
0.1%
335984 219
0.1%
335985 246
0.1%
335986 240
0.1%
335987 241
0.1%
335988 205
0.1%
335989 255
0.1%
335990 248
0.1%
335991 250
0.1%
ValueCountFrequency (%)
1426312 184
0.1%
1426311 251
0.1%
1426310 241
0.1%
1426309 208
0.1%
1426307 247
0.1%
1426306 253
0.1%
1426305 259
0.1%
1426303 235
0.1%
1426302 253
0.1%
1426300 247
0.1%

inning
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4835314
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-03-12T22:15:00.087952image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile2
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5026432
Coefficient of variation (CV)0.33881535
Kurtosis-1.6485018
Mean1.4835314
Median Absolute Deviation (MAD)0
Skewness0.12647854
Sum387083
Variance0.25265019
MonotonicityNot monotonic
2025-03-12T22:15:00.156196image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 135018
51.7%
2 125741
48.2%
3 77
 
< 0.1%
4 72
 
< 0.1%
5 8
 
< 0.1%
6 4
 
< 0.1%
ValueCountFrequency (%)
1 135018
51.7%
2 125741
48.2%
3 77
 
< 0.1%
4 72
 
< 0.1%
5 8
 
< 0.1%
6 4
 
< 0.1%
ValueCountFrequency (%)
6 4
 
< 0.1%
5 8
 
< 0.1%
4 72
 
< 0.1%
3 77
 
< 0.1%
2 125741
48.2%
1 135018
51.7%

batting_team
Categorical

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.6 MiB
Mumbai Indians
31437 
Kolkata Knight Riders
29514 
Chennai Super Kings
28651 
Royal Challengers Bangalore
28205 
Rajasthan Royals
26242 
Other values (14)
116871 

Length

Max length27
Median length22
Mean length17.908248
Min length12

Characters and Unicode

Total characters4672620
Distinct characters38
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKolkata Knight Riders
2nd rowKolkata Knight Riders
3rd rowKolkata Knight Riders
4th rowKolkata Knight Riders
5th rowKolkata Knight Riders

Common Values

ValueCountFrequency (%)
Mumbai Indians 31437
12.0%
Kolkata Knight Riders 29514
11.3%
Chennai Super Kings 28651
11.0%
Royal Challengers Bangalore 28205
10.8%
Rajasthan Royals 26242
10.1%
Kings XI Punjab 22646
8.7%
Sunrisers Hyderabad 21843
8.4%
Delhi Daredevils 18786
7.2%
Delhi Capitals 10946
 
4.2%
Deccan Chargers 9034
 
3.5%
Other values (9) 33616
12.9%

Length

2025-03-12T22:15:00.240241image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kings 58130
 
9.0%
super 34051
 
5.3%
mumbai 31437
 
4.9%
indians 31437
 
4.9%
royal 30023
 
4.7%
challengers 30023
 
4.7%
delhi 29732
 
4.6%
riders 29514
 
4.6%
knight 29514
 
4.6%
kolkata 29514
 
4.6%
Other values (26) 309761
48.2%

Most occurring characters

ValueCountFrequency (%)
a 546807
 
11.7%
n 390207
 
8.4%
382216
 
8.2%
e 326910
 
7.0%
i 321915
 
6.9%
s 312167
 
6.7%
r 258027
 
5.5%
l 236894
 
5.1%
g 163684
 
3.5%
h 154778
 
3.3%
Other values (28) 1579015
33.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3624622
77.6%
Uppercase Letter 665782
 
14.2%
Space Separator 382216
 
8.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 546807
15.1%
n 390207
10.8%
e 326910
9.0%
i 321915
8.9%
s 312167
8.6%
r 258027
 
7.1%
l 236894
 
6.5%
g 163684
 
4.5%
h 154778
 
4.3%
u 148891
 
4.1%
Other values (12) 764342
21.1%
Uppercase Letter
ValueCountFrequency (%)
K 120322
18.1%
R 115501
17.3%
C 78654
11.8%
S 59374
8.9%
D 57552
8.6%
I 54083
8.1%
P 38402
 
5.8%
M 31437
 
4.7%
B 30023
 
4.5%
X 22646
 
3.4%
Other values (5) 57788
8.7%
Space Separator
ValueCountFrequency (%)
382216
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4290404
91.8%
Common 382216
 
8.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 546807
 
12.7%
n 390207
 
9.1%
e 326910
 
7.6%
i 321915
 
7.5%
s 312167
 
7.3%
r 258027
 
6.0%
l 236894
 
5.5%
g 163684
 
3.8%
h 154778
 
3.6%
u 148891
 
3.5%
Other values (27) 1430124
33.3%
Common
ValueCountFrequency (%)
382216
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4672620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 546807
 
11.7%
n 390207
 
8.4%
382216
 
8.2%
e 326910
 
7.0%
i 321915
 
6.9%
s 312167
 
6.7%
r 258027
 
5.5%
l 236894
 
5.1%
g 163684
 
3.5%
h 154778
 
3.3%
Other values (28) 1579015
33.8%

bowling_team
Categorical

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.7 MiB
Mumbai Indians
31505 
Kolkata Knight Riders
29663 
Chennai Super Kings
28576 
Royal Challengers Bangalore
28358 
Rajasthan Royals
26432 
Other values (14)
116386 

Length

Max length27
Median length22
Mean length17.915254
Min length12

Characters and Unicode

Total characters4674448
Distinct characters38
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRoyal Challengers Bangalore
2nd rowRoyal Challengers Bangalore
3rd rowRoyal Challengers Bangalore
4th rowRoyal Challengers Bangalore
5th rowRoyal Challengers Bangalore

Common Values

ValueCountFrequency (%)
Mumbai Indians 31505
12.1%
Kolkata Knight Riders 29663
11.4%
Chennai Super Kings 28576
11.0%
Royal Challengers Bangalore 28358
10.9%
Rajasthan Royals 26432
10.1%
Kings XI Punjab 22483
8.6%
Sunrisers Hyderabad 21717
8.3%
Delhi Daredevils 18725
7.2%
Delhi Capitals 11216
 
4.3%
Deccan Chargers 9039
 
3.5%
Other values (9) 33206
12.7%

Length

2025-03-12T22:15:00.324890image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kings 57778
 
9.0%
super 33802
 
5.3%
mumbai 31505
 
4.9%
indians 31505
 
4.9%
royal 30159
 
4.7%
challengers 30159
 
4.7%
delhi 29941
 
4.7%
riders 29663
 
4.6%
knight 29663
 
4.6%
kolkata 29663
 
4.6%
Other values (26) 309266
48.1%

Most occurring characters

ValueCountFrequency (%)
a 547793
 
11.7%
n 389695
 
8.3%
382184
 
8.2%
e 327192
 
7.0%
i 322061
 
6.9%
s 312298
 
6.7%
r 257725
 
5.5%
l 238227
 
5.1%
g 163884
 
3.5%
h 155424
 
3.3%
Other values (28) 1577965
33.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3626677
77.6%
Uppercase Letter 665587
 
14.2%
Space Separator 382184
 
8.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 547793
15.1%
n 389695
10.7%
e 327192
9.0%
i 322061
8.9%
s 312298
8.6%
r 257725
 
7.1%
l 238227
 
6.6%
g 163884
 
4.5%
h 155424
 
4.3%
u 148057
 
4.1%
Other values (12) 764321
21.1%
Uppercase Letter
ValueCountFrequency (%)
K 120332
18.1%
R 116229
17.5%
C 78990
11.9%
S 59062
8.9%
D 57705
8.7%
I 53988
8.1%
P 38202
 
5.7%
M 31505
 
4.7%
B 30159
 
4.5%
X 22483
 
3.4%
Other values (5) 56932
8.6%
Space Separator
ValueCountFrequency (%)
382184
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4292264
91.8%
Common 382184
 
8.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 547793
 
12.8%
n 389695
 
9.1%
e 327192
 
7.6%
i 322061
 
7.5%
s 312298
 
7.3%
r 257725
 
6.0%
l 238227
 
5.6%
g 163884
 
3.8%
h 155424
 
3.6%
u 148057
 
3.4%
Other values (27) 1429908
33.3%
Common
ValueCountFrequency (%)
382184
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4674448
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 547793
 
11.7%
n 389695
 
8.3%
382184
 
8.2%
e 327192
 
7.0%
i 322061
 
6.9%
s 312298
 
6.7%
r 257725
 
5.5%
l 238227
 
5.1%
g 163884
 
3.5%
h 155424
 
3.3%
Other values (28) 1577965
33.8%

over
Real number (ℝ)

Zeros 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1976774
Minimum0
Maximum19
Zeros13906
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-03-12T22:15:00.397063image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median9
Q314
95-th percentile18
Maximum19
Range19
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.6834837
Coefficient of variation (CV)0.61792597
Kurtosis-1.1856269
Mean9.1976774
Median Absolute Deviation (MAD)5
Skewness0.041707947
Sum2399858
Variance32.301987
MonotonicityNot monotonic
2025-03-12T22:15:00.473694image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 13906
 
5.3%
1 13773
 
5.3%
2 13597
 
5.2%
3 13575
 
5.2%
4 13560
 
5.2%
5 13494
 
5.2%
6 13452
 
5.2%
7 13430
 
5.1%
8 13396
 
5.1%
9 13354
 
5.1%
Other values (10) 125383
48.1%
ValueCountFrequency (%)
0 13906
5.3%
1 13773
5.3%
2 13597
5.2%
3 13575
5.2%
4 13560
5.2%
5 13494
5.2%
6 13452
5.2%
7 13430
5.1%
8 13396
5.1%
9 13354
5.1%
ValueCountFrequency (%)
19 9998
3.8%
18 11583
4.4%
17 12318
4.7%
16 12685
4.9%
15 12879
4.9%
14 13024
5.0%
13 13124
5.0%
12 13222
5.1%
11 13261
5.1%
10 13289
5.1%

ball
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6244864
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-03-12T22:15:00.549185image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile6
Maximum11
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8149205
Coefficient of variation (CV)0.50073865
Kurtosis-1.053745
Mean3.6244864
Median Absolute Deviation (MAD)2
Skewness0.10712572
Sum945701
Variance3.2939363
MonotonicityNot monotonic
2025-03-12T22:15:00.611851image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 42210
16.2%
2 42106
16.1%
3 42002
16.1%
4 41891
16.1%
5 41752
16.0%
6 41619
16.0%
7 7776
 
3.0%
8 1307
 
0.5%
9 225
 
0.1%
10 30
 
< 0.1%
ValueCountFrequency (%)
1 42210
16.2%
2 42106
16.1%
3 42002
16.1%
4 41891
16.1%
5 41752
16.0%
6 41619
16.0%
7 7776
 
3.0%
8 1307
 
0.5%
9 225
 
0.1%
10 30
 
< 0.1%
ValueCountFrequency (%)
11 2
 
< 0.1%
10 30
 
< 0.1%
9 225
 
0.1%
8 1307
 
0.5%
7 7776
 
3.0%
6 41619
16.0%
5 41752
16.0%
4 41891
16.1%
3 42002
16.1%
2 42106
16.1%

batter
Text

Distinct673
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size14.5 MiB
2025-03-12T22:15:00.879528image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length23
Median length19
Mean length9.4247854
Min length5

Characters and Unicode

Total characters2459115
Distinct characters57
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)< 0.1%

Sample

1st rowSC Ganguly
2nd rowBB McCullum
3rd rowBB McCullum
4th rowBB McCullum
5th rowBB McCullum
ValueCountFrequency (%)
s 8992
 
1.7%
v 8965
 
1.7%
sharma 7691
 
1.4%
da 7020
 
1.3%
singh 7014
 
1.3%
kohli 6256
 
1.2%
de 6231
 
1.2%
r 6145
 
1.1%
dhawan 5671
 
1.1%
rg 5185
 
1.0%
Other values (894) 467539
87.1%
2025-03-12T22:15:01.279429image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 278809
 
11.3%
275789
 
11.2%
i 119472
 
4.9%
h 113155
 
4.6%
n 112566
 
4.6%
r 107131
 
4.4%
S 100549
 
4.1%
e 99075
 
4.0%
l 95230
 
3.9%
s 69740
 
2.8%
Other values (47) 1087599
44.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1451212
59.0%
Uppercase Letter 731601
29.8%
Space Separator 275789
 
11.2%
Dash Punctuation 501
 
< 0.1%
Open Punctuation 4
 
< 0.1%
Decimal Number 4
 
< 0.1%
Close Punctuation 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 278809
19.2%
i 119472
 
8.2%
h 113155
 
7.8%
n 112566
 
7.8%
r 107131
 
7.4%
e 99075
 
6.8%
l 95230
 
6.6%
s 69740
 
4.8%
d 57396
 
4.0%
o 57231
 
3.9%
Other values (16) 341407
23.5%
Uppercase Letter
ValueCountFrequency (%)
S 100549
13.7%
R 67019
 
9.2%
A 60180
 
8.2%
K 59494
 
8.1%
M 58918
 
8.1%
P 53997
 
7.4%
D 49721
 
6.8%
J 34928
 
4.8%
G 32430
 
4.4%
V 30549
 
4.2%
Other values (16) 183816
25.1%
Space Separator
ValueCountFrequency (%)
275789
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 501
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Decimal Number
ValueCountFrequency (%)
2 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2182813
88.8%
Common 276302
 
11.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 278809
 
12.8%
i 119472
 
5.5%
h 113155
 
5.2%
n 112566
 
5.2%
r 107131
 
4.9%
S 100549
 
4.6%
e 99075
 
4.5%
l 95230
 
4.4%
s 69740
 
3.2%
R 67019
 
3.1%
Other values (42) 1020067
46.7%
Common
ValueCountFrequency (%)
275789
99.8%
- 501
 
0.2%
( 4
 
< 0.1%
2 4
 
< 0.1%
) 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2459115
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 278809
 
11.3%
275789
 
11.2%
i 119472
 
4.9%
h 113155
 
4.6%
n 112566
 
4.6%
r 107131
 
4.4%
S 100549
 
4.1%
e 99075
 
4.0%
l 95230
 
3.9%
s 69740
 
2.8%
Other values (47) 1087599
44.2%

bowler
Text

Distinct530
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.6 MiB
2025-03-12T22:15:01.571534image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length23
Median length18
Mean length9.7543423
Min length5

Characters and Unicode

Total characters2545103
Distinct characters55
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowP Kumar
2nd rowP Kumar
3rd rowP Kumar
4th rowP Kumar
5th rowP Kumar
ValueCountFrequency (%)
r 12556
 
2.4%
sharma 12256
 
2.3%
singh 11252
 
2.1%
a 10113
 
1.9%
kumar 9490
 
1.8%
m 9427
 
1.8%
khan 7437
 
1.4%
s 7205
 
1.4%
patel 6838
 
1.3%
pp 5840
 
1.1%
Other values (729) 438059
82.6%
2025-03-12T22:15:01.954262image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 327701
 
12.9%
269553
 
10.6%
h 143316
 
5.6%
r 135369
 
5.3%
n 128319
 
5.0%
e 118583
 
4.7%
i 110331
 
4.3%
S 92216
 
3.6%
l 76231
 
3.0%
M 65127
 
2.6%
Other values (45) 1078357
42.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1585511
62.3%
Uppercase Letter 688249
27.0%
Space Separator 269553
 
10.6%
Dash Punctuation 1751
 
0.1%
Open Punctuation 13
 
< 0.1%
Decimal Number 13
 
< 0.1%
Close Punctuation 13
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 327701
20.7%
h 143316
9.0%
r 135369
 
8.5%
n 128319
 
8.1%
e 118583
 
7.5%
i 110331
 
7.0%
l 76231
 
4.8%
s 63765
 
4.0%
d 63506
 
4.0%
m 62428
 
3.9%
Other values (16) 355962
22.5%
Uppercase Letter
ValueCountFrequency (%)
S 92216
13.4%
M 65127
 
9.5%
A 61221
 
8.9%
P 54305
 
7.9%
K 51694
 
7.5%
R 50981
 
7.4%
J 44795
 
6.5%
B 33139
 
4.8%
C 32925
 
4.8%
D 30707
 
4.5%
Other values (14) 171139
24.9%
Space Separator
ValueCountFrequency (%)
269553
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1751
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Decimal Number
ValueCountFrequency (%)
2 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2273760
89.3%
Common 271343
 
10.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 327701
 
14.4%
h 143316
 
6.3%
r 135369
 
6.0%
n 128319
 
5.6%
e 118583
 
5.2%
i 110331
 
4.9%
S 92216
 
4.1%
l 76231
 
3.4%
M 65127
 
2.9%
s 63765
 
2.8%
Other values (40) 1012802
44.5%
Common
ValueCountFrequency (%)
269553
99.3%
- 1751
 
0.6%
( 13
 
< 0.1%
2 13
 
< 0.1%
) 13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2545103
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 327701
 
12.9%
269553
 
10.6%
h 143316
 
5.6%
r 135369
 
5.3%
n 128319
 
5.0%
e 118583
 
4.7%
i 110331
 
4.3%
S 92216
 
3.6%
l 76231
 
3.0%
M 65127
 
2.6%
Other values (45) 1078357
42.4%
Distinct663
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size14.5 MiB
2025-03-12T22:15:02.237287image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length23
Median length19
Mean length9.4367737
Min length5

Characters and Unicode

Total characters2462243
Distinct characters57
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st rowBB McCullum
2nd rowSC Ganguly
3rd rowSC Ganguly
4th rowSC Ganguly
5th rowSC Ganguly
ValueCountFrequency (%)
s 9231
 
1.7%
v 8905
 
1.7%
sharma 7804
 
1.5%
da 6787
 
1.3%
singh 6717
 
1.3%
de 6137
 
1.1%
dhawan 6108
 
1.1%
kohli 6089
 
1.1%
r 6058
 
1.1%
mk 5308
 
1.0%
Other values (887) 467943
87.1%
2025-03-12T22:15:02.744508image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 280963
 
11.4%
276167
 
11.2%
i 119335
 
4.8%
h 114211
 
4.6%
n 112947
 
4.6%
r 106873
 
4.3%
S 100776
 
4.1%
e 99512
 
4.0%
l 94571
 
3.8%
s 70157
 
2.8%
Other values (47) 1086731
44.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1454743
59.1%
Uppercase Letter 730835
29.7%
Space Separator 276167
 
11.2%
Dash Punctuation 495
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Decimal Number 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 280963
19.3%
i 119335
 
8.2%
h 114211
 
7.9%
n 112947
 
7.8%
r 106873
 
7.3%
e 99512
 
6.8%
l 94571
 
6.5%
s 70157
 
4.8%
d 57989
 
4.0%
o 55673
 
3.8%
Other values (16) 342512
23.5%
Uppercase Letter
ValueCountFrequency (%)
S 100776
13.8%
R 66769
 
9.1%
K 59848
 
8.2%
A 59732
 
8.2%
M 59101
 
8.1%
P 53390
 
7.3%
D 48912
 
6.7%
J 34759
 
4.8%
G 32907
 
4.5%
V 30945
 
4.2%
Other values (16) 183696
25.1%
Space Separator
ValueCountFrequency (%)
276167
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 495
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2185578
88.8%
Common 276665
 
11.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 280963
 
12.9%
i 119335
 
5.5%
h 114211
 
5.2%
n 112947
 
5.2%
r 106873
 
4.9%
S 100776
 
4.6%
e 99512
 
4.6%
l 94571
 
4.3%
s 70157
 
3.2%
R 66769
 
3.1%
Other values (42) 1019464
46.6%
Common
ValueCountFrequency (%)
276167
99.8%
- 495
 
0.2%
( 1
 
< 0.1%
2 1
 
< 0.1%
) 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2462243
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 280963
 
11.4%
276167
 
11.2%
i 119335
 
4.8%
h 114211
 
4.6%
n 112947
 
4.6%
r 106873
 
4.3%
S 100776
 
4.1%
e 99512
 
4.0%
l 94571
 
3.8%
s 70157
 
2.8%
Other values (47) 1086731
44.1%

batsman_runs
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2650008
Minimum0
Maximum6
Zeros103940
Zeros (%)39.8%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-03-12T22:15:02.842458image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6392976
Coefficient of variation (CV)1.2958867
Kurtosis1.5226807
Mean1.2650008
Median Absolute Deviation (MAD)1
Skewness1.5642578
Sum330064
Variance2.6872968
MonotonicityNot monotonic
2025-03-12T22:15:02.904910image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 103940
39.8%
1 96778
37.1%
4 29850
 
11.4%
2 16453
 
6.3%
6 13051
 
5.0%
3 783
 
0.3%
5 65
 
< 0.1%
ValueCountFrequency (%)
0 103940
39.8%
1 96778
37.1%
2 16453
 
6.3%
3 783
 
0.3%
4 29850
 
11.4%
5 65
 
< 0.1%
6 13051
 
5.0%
ValueCountFrequency (%)
6 13051
 
5.0%
5 65
 
< 0.1%
4 29850
 
11.4%
3 783
 
0.3%
2 16453
 
6.3%
1 96778
37.1%
0 103940
39.8%

extra_runs
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.067806224
Minimum0
Maximum7
Zeros246795
Zeros (%)94.6%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-03-12T22:15:02.968216image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.34326535
Coefficient of variation (CV)5.062446
Kurtosis90.549735
Mean0.067806224
Median Absolute Deviation (MAD)0
Skewness8.1879494
Sum17692
Variance0.1178311
MonotonicityNot monotonic
2025-03-12T22:15:03.032852image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 246795
94.6%
1 12628
 
4.8%
2 585
 
0.2%
4 504
 
0.2%
5 325
 
0.1%
3 82
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 246795
94.6%
1 12628
 
4.8%
2 585
 
0.2%
3 82
 
< 0.1%
4 504
 
0.2%
5 325
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
5 325
 
0.1%
4 504
 
0.2%
3 82
 
< 0.1%
2 585
 
0.2%
1 12628
 
4.8%
0 246795
94.6%

total_runs
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.332807
Minimum0
Maximum7
Zeros90438
Zeros (%)34.7%
Negative0
Negative (%)0.0%
Memory size2.0 MiB
2025-03-12T22:15:03.098365image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6264158
Coefficient of variation (CV)1.2202936
Kurtosis1.4714525
Mean1.332807
Median Absolute Deviation (MAD)1
Skewness1.5363123
Sum347756
Variance2.6452285
MonotonicityNot monotonic
2025-03-12T22:15:03.166482image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 108440
41.6%
0 90438
34.7%
4 30221
 
11.6%
2 17323
 
6.6%
6 12964
 
5.0%
3 922
 
0.4%
5 524
 
0.2%
7 88
 
< 0.1%
ValueCountFrequency (%)
0 90438
34.7%
1 108440
41.6%
2 17323
 
6.6%
3 922
 
0.4%
4 30221
 
11.6%
5 524
 
0.2%
6 12964
 
5.0%
7 88
 
< 0.1%
ValueCountFrequency (%)
7 88
 
< 0.1%
6 12964
 
5.0%
5 524
 
0.2%
4 30221
 
11.6%
3 922
 
0.4%
2 17323
 
6.6%
1 108440
41.6%
0 90438
34.7%

is_wicket
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.4 MiB
0
247970 
1
 
12950

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 247970
95.0%
1 12950
 
5.0%

Length

2025-03-12T22:15:03.240722image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-12T22:15:03.305297image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 247970
95.0%
1 12950
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 247970
95.0%
1 12950
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260920
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 247970
95.0%
1 12950
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Common 260920
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 247970
95.0%
1 12950
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 247970
95.0%
1 12950
 
5.0%
Distinct630
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.1 MiB
2025-03-12T22:15:03.577801image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length23
Median length12
Mean length11.87495
Min length5

Characters and Unicode

Total characters3098412
Distinct characters57
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique103 ?
Unique (%)< 0.1%

Sample

1st rowNo dismissal
2nd rowNo dismissal
3rd rowNo dismissal
4th rowNo dismissal
5th rowNo dismissal
ValueCountFrequency (%)
no 247970
47.5%
dismissal 247970
47.5%
singh 434
 
0.1%
sharma 409
 
0.1%
s 388
 
0.1%
r 379
 
0.1%
v 351
 
0.1%
m 277
 
0.1%
de 260
 
< 0.1%
da 246
 
< 0.1%
Other values (845) 23851
 
4.6%
2025-03-12T22:15:03.992326image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 747238
24.1%
i 501821
16.2%
a 262223
 
8.5%
261615
 
8.4%
l 252391
 
8.1%
o 250886
 
8.1%
d 250828
 
8.1%
m 250222
 
8.1%
N 248717
 
8.0%
h 5894
 
0.2%
Other values (47) 66577
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2552660
82.4%
Uppercase Letter 284094
 
9.2%
Space Separator 261615
 
8.4%
Dash Punctuation 40
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Decimal Number 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 747238
29.3%
i 501821
19.7%
a 262223
 
10.3%
l 252391
 
9.9%
o 250886
 
9.8%
d 250828
 
9.8%
m 250222
 
9.8%
h 5894
 
0.2%
n 5626
 
0.2%
r 5512
 
0.2%
Other values (16) 20019
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
N 248717
87.5%
S 4939
 
1.7%
R 3242
 
1.1%
A 3103
 
1.1%
M 3006
 
1.1%
P 2830
 
1.0%
K 2819
 
1.0%
D 2254
 
0.8%
J 1815
 
0.6%
G 1462
 
0.5%
Other values (16) 9907
 
3.5%
Space Separator
ValueCountFrequency (%)
261615
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 40
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2836754
91.6%
Common 261658
 
8.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 747238
26.3%
i 501821
17.7%
a 262223
 
9.2%
l 252391
 
8.9%
o 250886
 
8.8%
d 250828
 
8.8%
m 250222
 
8.8%
N 248717
 
8.8%
h 5894
 
0.2%
n 5626
 
0.2%
Other values (42) 60908
 
2.1%
Common
ValueCountFrequency (%)
261615
> 99.9%
- 40
 
< 0.1%
( 1
 
< 0.1%
2 1
 
< 0.1%
) 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3098412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 747238
24.1%
i 501821
16.2%
a 262223
 
8.5%
261615
 
8.4%
l 252391
 
8.1%
o 250886
 
8.1%
d 250828
 
8.1%
m 250222
 
8.1%
N 248717
 
8.0%
h 5894
 
0.2%
Other values (47) 66577
 
2.1%

dismissal_kind
Categorical

High correlation  Imbalance 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.1 MiB
No dismissal
247970 
caught
 
8063
bowled
 
2212
run out
 
1114
lbw
 
800
Other values (6)
 
761

Length

Max length21
Median length12
Mean length11.714928
Min length3

Characters and Unicode

Total characters3056659
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo dismissal
2nd rowNo dismissal
3rd rowNo dismissal
4th rowNo dismissal
5th rowNo dismissal

Common Values

ValueCountFrequency (%)
No dismissal 247970
95.0%
caught 8063
 
3.1%
bowled 2212
 
0.8%
run out 1114
 
0.4%
lbw 800
 
0.3%
caught and bowled 367
 
0.1%
stumped 358
 
0.1%
retired hurt 15
 
< 0.1%
hit wicket 15
 
< 0.1%
obstructing the field 3
 
< 0.1%

Length

2025-03-12T22:15:04.114701image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no 247970
48.5%
dismissal 247970
48.5%
caught 8430
 
1.7%
bowled 2579
 
0.5%
out 1117
 
0.2%
run 1114
 
0.2%
lbw 800
 
0.2%
and 367
 
0.1%
stumped 358
 
0.1%
retired 18
 
< 0.1%
Other values (6) 54
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
s 744271
24.3%
i 495994
16.2%
a 256767
 
8.4%
o 251669
 
8.2%
l 251352
 
8.2%
d 251295
 
8.2%
249857
 
8.2%
m 248328
 
8.1%
N 247970
 
8.1%
u 11037
 
0.4%
Other values (12) 48119
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2558832
83.7%
Space Separator 249857
 
8.2%
Uppercase Letter 247970
 
8.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 744271
29.1%
i 495994
19.4%
a 256767
 
10.0%
o 251669
 
9.8%
l 251352
 
9.8%
d 251295
 
9.8%
m 248328
 
9.7%
u 11037
 
0.4%
t 9977
 
0.4%
h 8463
 
0.3%
Other values (10) 29679
 
1.2%
Space Separator
ValueCountFrequency (%)
249857
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 247970
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2806802
91.8%
Common 249857
 
8.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 744271
26.5%
i 495994
17.7%
a 256767
 
9.1%
o 251669
 
9.0%
l 251352
 
9.0%
d 251295
 
9.0%
m 248328
 
8.8%
N 247970
 
8.8%
u 11037
 
0.4%
t 9977
 
0.4%
Other values (11) 38142
 
1.4%
Common
ValueCountFrequency (%)
249857
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3056659
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 744271
24.3%
i 495994
16.2%
a 256767
 
8.4%
o 251669
 
8.2%
l 251352
 
8.2%
d 251295
 
8.2%
249857
 
8.2%
m 248328
 
8.1%
N 247970
 
8.1%
u 11037
 
0.4%
Other values (12) 48119
 
1.6%
Distinct608
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size14.7 MiB
2025-03-12T22:15:04.329819image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length23
Median length10
Mean length9.9829526
Min length5

Characters and Unicode

Total characters2604752
Distinct characters53
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)< 0.1%

Sample

1st rowNo fielder
2nd rowNo fielder
3rd rowNo fielder
4th rowNo fielder
5th rowNo fielder
ValueCountFrequency (%)
no 251566
48.2%
fielder 251566
48.2%
r 308
 
0.1%
singh 290
 
0.1%
sharma 281
 
0.1%
ms 265
 
0.1%
de 247
 
< 0.1%
m 244
 
< 0.1%
dhoni 220
 
< 0.1%
patel 217
 
< 0.1%
Other values (819) 17163
 
3.3%
2025-03-12T22:15:04.674665image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 506747
19.5%
261447
10.0%
i 255979
9.8%
r 255597
9.8%
l 254717
9.8%
o 253830
9.7%
d 253724
9.7%
N 252106
9.7%
f 251630
9.7%
a 10506
 
0.4%
Other values (43) 48469
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2065808
79.3%
Uppercase Letter 277460
 
10.7%
Space Separator 261447
 
10.0%
Dash Punctuation 37
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 506747
24.5%
i 255979
12.4%
r 255597
12.4%
l 254717
12.3%
o 253830
12.3%
d 253724
12.3%
f 251630
12.2%
a 10506
 
0.5%
h 4485
 
0.2%
n 3988
 
0.2%
Other values (16) 14605
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
N 252106
90.9%
S 3489
 
1.3%
K 2295
 
0.8%
R 2272
 
0.8%
M 2212
 
0.8%
A 2170
 
0.8%
P 1987
 
0.7%
D 1779
 
0.6%
J 1307
 
0.5%
B 1071
 
0.4%
Other values (15) 6772
 
2.4%
Space Separator
ValueCountFrequency (%)
261447
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 37
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2343268
90.0%
Common 261484
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 506747
21.6%
i 255979
10.9%
r 255597
10.9%
l 254717
10.9%
o 253830
10.8%
d 253724
10.8%
N 252106
10.8%
f 251630
10.7%
a 10506
 
0.4%
h 4485
 
0.2%
Other values (41) 43947
 
1.9%
Common
ValueCountFrequency (%)
261447
> 99.9%
- 37
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2604752
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 506747
19.5%
261447
10.0%
i 255979
9.8%
r 255597
9.8%
l 254717
9.8%
o 253830
9.7%
d 253724
9.7%
N 252106
9.7%
f 251630
9.7%
a 10506
 
0.4%
Other values (43) 48469
 
1.9%

Interactions

2025-03-12T22:14:58.129816image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:54.370426image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:55.181841image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:55.735773image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:56.330965image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:56.887506image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:57.561357image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:58.213269image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:54.453156image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:55.261627image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:55.820855image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:56.408577image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:56.972735image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:57.640849image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:58.291952image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:54.533903image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:55.337751image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:55.903545image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:56.485109image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:57.054148image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:57.719706image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:58.378304image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:54.619568image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:55.418172image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:55.988344image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:56.567168image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:57.230751image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:57.803471image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:58.456172image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:54.697297image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:55.495019image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:56.069987image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:56.639791image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:57.308698image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:57.881390image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:58.539608image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:54.779117image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:55.573072image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:56.154249image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:56.718952image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:57.392552image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:57.961630image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:58.631877image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:54.861960image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:55.654738image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:56.240716image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:56.798643image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:57.478531image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-12T22:14:58.044783image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Correlations

2025-03-12T22:15:04.765130image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ballbatsman_runsbatting_teambowling_teamdismissal_kindextra_runsinningis_wicketmatch_idovertotal_runs
ball1.0000.0070.0040.0000.005-0.001-0.0050.0030.004-0.0020.006
batsman_runs0.0071.0000.0170.0130.111-0.245-0.0100.2670.0320.1200.939
batting_team0.0040.0171.0000.1330.0060.0050.0330.0100.3290.0000.016
bowling_team0.0000.0130.1331.0000.0060.0070.0350.0050.3290.0000.012
dismissal_kind0.0050.1110.0060.0061.0000.0210.0101.0000.0080.0360.114
extra_runs-0.001-0.2450.0050.0070.0211.000-0.0010.050-0.0020.0170.090
inning-0.005-0.0100.0330.0350.010-0.0011.0000.0160.001-0.047-0.010
is_wicket0.0030.2670.0100.0051.0000.0500.0161.0000.0030.0910.297
match_id0.0040.0320.3290.3290.008-0.0020.0010.0031.0000.0110.031
over-0.0020.1200.0000.0000.0360.017-0.0470.0910.0111.0000.125
total_runs0.0060.9390.0160.0120.1140.090-0.0100.2970.0310.1251.000

Missing values

2025-03-12T22:14:58.806175image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-12T22:14:59.160697image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

match_idinningbatting_teambowling_teamoverballbatterbowlernon_strikerbatsman_runsextra_runstotal_runsis_wicketplayer_dismisseddismissal_kindfielder
03359821Kolkata Knight RidersRoyal Challengers Bangalore01SC GangulyP KumarBB McCullum0110No dismissalNo dismissalNo fielder
13359821Kolkata Knight RidersRoyal Challengers Bangalore02BB McCullumP KumarSC Ganguly0000No dismissalNo dismissalNo fielder
23359821Kolkata Knight RidersRoyal Challengers Bangalore03BB McCullumP KumarSC Ganguly0110No dismissalNo dismissalNo fielder
33359821Kolkata Knight RidersRoyal Challengers Bangalore04BB McCullumP KumarSC Ganguly0000No dismissalNo dismissalNo fielder
43359821Kolkata Knight RidersRoyal Challengers Bangalore05BB McCullumP KumarSC Ganguly0000No dismissalNo dismissalNo fielder
53359821Kolkata Knight RidersRoyal Challengers Bangalore06BB McCullumP KumarSC Ganguly0000No dismissalNo dismissalNo fielder
63359821Kolkata Knight RidersRoyal Challengers Bangalore07BB McCullumP KumarSC Ganguly0110No dismissalNo dismissalNo fielder
73359821Kolkata Knight RidersRoyal Challengers Bangalore11BB McCullumZ KhanSC Ganguly0000No dismissalNo dismissalNo fielder
83359821Kolkata Knight RidersRoyal Challengers Bangalore12BB McCullumZ KhanSC Ganguly4040No dismissalNo dismissalNo fielder
93359821Kolkata Knight RidersRoyal Challengers Bangalore13BB McCullumZ KhanSC Ganguly4040No dismissalNo dismissalNo fielder
match_idinningbatting_teambowling_teamoverballbatterbowlernon_strikerbatsman_runsextra_runstotal_runsis_wicketplayer_dismisseddismissal_kindfielder
26091014263122Kolkata Knight RidersSunrisers Hyderabad86SS IyerShahbaz AhmedVR Iyer4040No dismissalNo dismissalNo fielder
26091114263122Kolkata Knight RidersSunrisers Hyderabad91VR IyerAK MarkramSS Iyer2020No dismissalNo dismissalNo fielder
26091214263122Kolkata Knight RidersSunrisers Hyderabad92VR IyerAK MarkramSS Iyer0000No dismissalNo dismissalNo fielder
26091314263122Kolkata Knight RidersSunrisers Hyderabad93VR IyerAK MarkramSS Iyer0000No dismissalNo dismissalNo fielder
26091414263122Kolkata Knight RidersSunrisers Hyderabad94VR IyerAK MarkramSS Iyer1010No dismissalNo dismissalNo fielder
26091514263122Kolkata Knight RidersSunrisers Hyderabad95SS IyerAK MarkramVR Iyer1010No dismissalNo dismissalNo fielder
26091614263122Kolkata Knight RidersSunrisers Hyderabad96VR IyerAK MarkramSS Iyer1010No dismissalNo dismissalNo fielder
26091714263122Kolkata Knight RidersSunrisers Hyderabad101VR IyerShahbaz AhmedSS Iyer1010No dismissalNo dismissalNo fielder
26091814263122Kolkata Knight RidersSunrisers Hyderabad102SS IyerShahbaz AhmedVR Iyer1010No dismissalNo dismissalNo fielder
26091914263122Kolkata Knight RidersSunrisers Hyderabad103VR IyerShahbaz AhmedSS Iyer1010No dismissalNo dismissalNo fielder